predict.ssanova {gss} | R Documentation |
Evaluate terms in a smoothing spline ANOVA fit at arbitrary points. Standard errors of the terms can be requested for use in constructing Bayesian confidence intervals.
predict[.ssanova](obj, newdata, se.fit=FALSE, include=obj$terms$labels)
obj |
Object of class inheriting from "ssanova" . |
newdata |
Data frame or model frame in which to predict. |
se.fit |
Flag indicating if standard errors are required. |
include |
List of model terms to be included in the
prediction. The partial and offset terms, if
present, are to be specified by "partial" and
"offset" , respectively. |
For se.fit=FALSE
, predict.ssanova
returns a vector of
the evaluated fit.
For se.fit=TRUE
, predict.ssanova
returns a list
consisting of the following components.
fit |
Vector of evaluated fit. |
se.fit |
Vector of standard errors. |
Chong Gu, chong@stat.purdue.edu
Gu, C. and Wahba, G. (1993), Smoothing spline ANOVA with component-wise Bayesian "confidence intervals". Journal of Computational and Graphical Statistics, 2, 97117.
Fitting functions ssanova
, ssanova
and
methods summary.ssanova
, summary.gssanova
,
fitted.ssanova
.
## Fit a model with thin-plate marginals, where geog is 2-D data(LakeAcidity) fit <- ssanova(ph~log(cal)*geog,"tp",LakeAcidity) ## Obtain estimates and standard errors on a grid new <- data.frame(cal=1,geog=I(matrix(0,1,2))) new <- model.frame(~log(cal)+geog,new) predict(fit,new,se=TRUE) ## Evaluate the geog main effect predict(fit,new,se=TRUE,inc="geog") ## Evaluate the sum of the geog main effect and the interaction predict(fit,new,se=TRUE,inc=c("geog","log(cal):geog")) ## Evaluate the geog main effect on a grid grid <- seq(-.04,.04,len=21) new <- model.frame(~geog,list(geog=cbind(rep(grid,21),rep(grid,rep(21,21))))) est <- predict(fit,new,se=TRUE,inc="geog") ## Plot the fit and standard error par(pty="s") contour(grid,grid,matrix(est$fit,21,21),col=1) contour(grid,grid,matrix(est$se,21,21),add=TRUE,col=2)